If you’re looking to make some money with sports betting, you could do worse than find a profitable betting system. There’s a good chance you’ve heard of such systems in the past, but you might be unsure about how they work, which is holding you back from diving in.
In this article, we’ll share our knowledge on how betting systems work, including powerful insights into the areas of Big Data, Machine Learning, and Bankroll Management. With any luck, you’ll walk away feeling more confident to put your money in an algorithm-based betting system.
Profitable betting systems are designed to find bets which have better odds than those available at bookmakers. As a result, a core element of any betting strategy is being able to accurately estimate the odds of a certain outcome, so that those odds can be compared to the bookmakers’.
The easiest way to estimate the odds of an outcome is by looking at past matches. For example, in football, if team A is to play team B, a good way to evaluate the match odds would be to look at past games between team A and B. Looking at past matches can be used not only to estimate the odds of a certain team winning, but also to estimate the likelihood of other outcomes, such as the number of goals scored.
Ideally, it’s best to compare matches that are similar (or the same). However, it’s also possible to gain insight from other sporting matches that feature one or more of the same participants. In the example above, we talked about looking at past matches between team A and team B. However, to improve the accuracy of the predictions, a betting system can also look at recent matches that featured just one of the teams.
If one of the teams is on a winning streak, the system will know to give them better odds. On the other hand, if one of the teams is on a losing streak, the system might give them worse odds.
Whether it’s data from matches that featured both teams or just one of them, creating an accurate model requires extremely large amounts of data — as much as is available! In fact, there’s a name for this field of data-heavy analysis: Big Data.
Another part of an effective betting system is Machine Learning. After all, regardless of how accurate the system’s models are, certain bets are likely to be incorrect from time to time. A good betting system will learn from these mistakes, using insight from its past performance to inform future decisions.
For example, past data might show that team A has a high chance of winning over team B, since that was the case in the past. However, organizational changes on the team, or simply changes in skill, might cause this to reverse. An average betting system, which would have previously favored team A, will start to create incorrect predictions.
If the system is quick to learn from these mistakes — and uses them to refine its data models for calculating odds — then it will be able to fix the issue quickly. However, without this crucial Machine Learning element, the system would continue to favor the previous winner until a substantial number of losses were incurred.
The next issue betting systems have is bankroll management — in other words, knowing how much to put on each bet. After all, good betting systems can create loads of accurate predictions, but they’re no good if you put all of your money on a single bet which ends up being incorrect. On the other hand, betting systems need to avoid putting too little money on each bet, which can hold back your ability to actually generate profits.
As a result, there’s a sweet spot somewhere in the middle, where the betting system can suggest stakes that aren’t too risky, but also not too passive. Of course, this depends on how big the bankroll is of the user who is betting. For a larger bankroll, a greater sum can be placed on each bet; for a smaller bankroll, a smaller sum can be placed on each bet (proportionally).
There’s another aspect of bankroll management that determines the profitability of a betting system. That’s knowing how likely an individual bet is to be correct. Instead of placing bets that are all the same size (based on the user’s bankroll), a good betting system should encourage users to place bigger stakes on the bets they are more sure of, and smaller stakes on the bets they are less sure of.
The mechanics behind this can be very complicated, but the basic idea is that the betting system — when finding profitable odds — determines how confident it is about the bet based on the data available.
Bookmakers vs Exchanges
Another dilemma betting systems face is whether their bets should be placed on regular bookmakers or betting exchanges. Regular bookmakers usually offer less favorable odds, since they require an edge in order to profit. With betting exchanges, however, the user’s bet is matched against another user who is willing to take the opposite side of the bet. As a result, the odds are usually better.
It’s worth bearing in mind that betting exchanges usually take a commission on any wins. For a betting system to be profitable, it needs to be able to overcome these commissions accounting for all the winning and losing bets it places.
There’s a lot that goes into building a profitable betting system, but it all starts with Big Data. By analyzing vast numbers of past sporting matches, the system is able to find outcomes with better odds than those available at bookmakers. In case of any losses, a good system should also use Machine Learning in order to refine its models. Finally, the system should take into account the user’s bankroll, scaling bets to the right size to ensure profitability.